# Dodo Digital — Full Site Content

> Source: https://www.dododigital.ai
> This document contains the full text content of dododigital.ai for AI agents and LLMs.

## Home

*AI systems consultancy*

### 95% of AI projects fail. We take that personally.

We build AI systems that go to production and stay there. They plug into your workflows, run on your data, and get smarter the longer they run.

### You've already tried AI. It's not your fault it didn't stick.

You sat through the demos. You ran the pilot. Nothing worked the way they promised it would. The problem isn't your technology or your data.

It's how it was implemented.

We're the firm companies call after that.

### How We Work

Four steps. From first call to production.

Every engagement follows the same playbook. It works because we've run it dozens of times across industries.

01. **Map** — We learn your business and find the highest-value AI opportunities. Days, not months.
02. **Build** — Two-week sprints. Each one ships working software on real data.
03. **Deploy** — Integrated with your existing stack. Your team uses it on day one.
04. **Support** — Six months of backup. If something breaks, we fix it.

### Case Studies

**Case Study — Private Equity Fund**

Two people run a PE fund. We built five AI systems that gave them back 40 hours a month.

- CIM analysis — due diligence that took days now takes minutes
- Capital calls — processed and tracked automatically
- Investor emails — drafted, personalized, sent on schedule
- Call logging — every call transcribed, tagged, pushed to CRM
- Portfolio briefs — one summary across all holdings, updated weekly

> “We stopped talking about hiring. The systems do what a third person would have done.”

**Case Study — Management Consulting**

A solo consultant had 20 years of contacts buried in old emails. We turned them into his biggest revenue channel.

- Relationship engine — built from emails, LinkedIn, and calendar history
- Smart nudges — surfaces who to reach out to and why
- Job change alerts — flags when contacts move roles
- Angle suggestions — recommends conversation starters based on context

> “Went from 20K a month to 76K in 30 days. Most of it came from people I already knew but forgot about.”

Your systems run on the same infrastructure we obsess over every day. We build it, we run it, we depend on it. Then we deploy the same thing for you.

*(If it didn't work, we'd be out of business. We're not.)*

### AI Newsletter

An AI newsletter that's different for every reader.

It scans Product Hunt, Reddit, Twitter, and web search every month. It writes an edition personalized to your role, your industry, and what you actually care about. It learns your preferences over time. The longer you read it, the sharper it gets.

### FAQ

**What does a typical engagement look like?**

We start with a strategy session to understand your business and find the highest-value opportunities. Then we build in two-week sprints. Most clients see working systems within the first sprint. After that, we either keep building or move to support.

**How do the two-week sprints work?**

We agree on what to build, then we build it. Each sprint ends with working software in production, not a presentation. You test it with real data, we iterate based on what you find, and we move to the next priority.

**We have an internal dev team. How does that work?**

We work alongside your team and transfer knowledge as we go. The goal is that your people can maintain and extend everything we build. We're not trying to create dependency.

**What happens when the engagement ends?**

You get six months of support included. If something breaks or needs tuning, we handle it. Most teams are running independently well before that window closes.

**Why should we trust a smaller firm with this?**

Because every project we take is our reputation. We don't have forty clients and a bench of junior associates. We have a small number of engagements and a founder in every one of them. The 95% failure rate comes from firms that can absorb a failed project and move on. We don't operate that way.

**How is this different from hiring an AI consultant?**

Most consultants hand you a strategy document. We hand you working systems. We also use everything we build across our own operations, so we have skin in the game on whether these tools actually hold up.

**What if we're not sure where AI fits in our business?**

That's what the strategy session is for. We've seen enough companies to spot patterns fast. We'll tell you what's worth building, what's not, and what order makes sense. If there's nothing worth doing right now, we'll tell you that too.

### Don't be the 95%.

Tell us what you're working on. We'll tell you where AI fits, what it would take to build, and whether we're the right people to do it.

*No commitment. If we're not the right fit, we'll tell you.*

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## The Systems We Run

Done-for-you AI dev infrastructure for technical teams.

Turn your developers into 10x AI engineers using the same systems, configs, and infrastructure we use to run our consulting practice. We built it for ourselves first. Now we deploy it for you.

### Three layers. All production.

Most teams get stuck at the same point: they've seen what AI can do in a demo, but they don't have the infrastructure to make it run reliably in their actual workflow.

We built that infrastructure for ourselves first. Every client engagement runs on the same three layers.

### 01. AI Coding Configs

*Your team’s AI is only as good as its configuration.*

Out of the box, tools like Claude Code, Cursor, Amp, Codex, and OpenCode are powerful but generalized. They don’t know your codebase, your conventions, or the things your team cares about. When they produce bad output, the issue is usually configuration, not capability.

We build custom AI coding configurations for development teams. These are systems that enforce your standards in real time, get more intelligent as you use them, and are customizable at both the admin level and the individual developer level. They generalize across any set of supported coding agents.

- Hooks that enforce your standards before code gets committed
- Skills that encode your team’s actual patterns and workflows
- LSP integration so the model understands your types, your APIs, your architecture
- CLI tooling that wraps common operations into repeatable commands
- Forbidden patterns so the model never produces code your team has agreed to avoid

**Result:** Every developer on your team works with the same AI configuration. You improve it together. The model gets better as your team uses it, not worse.

### 02. Agent Deployment Platform

*An AI agent is only useful if you can run it from the places where work actually happens.*

You need to trigger agents from Slack messages. From Linear issues. From GitHub comments. From emails. From cron jobs at 2 AM. Each one needs the right system prompt, the right skills, the right authentication, and the right guardrails.

We built a standardized deployment layer that handles all of this:

- One config per agent — system prompt, skills, auth, and tool access defined in one place
- Multi-source triggers — Slack, Linear, GitHub, email, webhooks, scheduled runs
- Isolated execution — each agent runs with exactly the permissions it needs and nothing else
- Version control — agent configs are code, reviewed and deployed like any other infrastructure

**Result:** This is the base layer for most of what we build. It’s why our systems run reliably and why they’re maintainable after we leave.

### 03. Always-On Cloud Agents

*Agents that run without anyone asking them to.*

We set up VPS instances that run long-lived agent processes. Orchestration harnesses manage parallel execution. Pipelines chain multi-step workflows. And because these run on your infrastructure, not ours, you own them.

What this looks like in practice:

- Bug triage — a Linear issue gets created, an agent reproduces it, opens a PR with a fix, and tags the right reviewer
- Code review — push to a branch, agents run security scans, pattern checks, and architectural review before a human looks at it
- Knowledge work — agents process incoming emails, update CRMs, generate reports, and flag anything that needs a human decision

**Result:** Every one of these can be fixed, extended, or shut down from a Slack message or a Linear comment. You’re never waiting on us.

### What We Build

For teams that already write code.

If you have developers, you don't need another AI platform. You need someone who's already built the infrastructure and can set it up for your team in weeks, not quarters.

**Custom AI Coding Config**

We audit how your team works. We read your codebase. We build a config that includes hooks, skills, forbidden patterns, and CLI tools specific to your stack and conventions. Every developer gets the same setup. You iterate on it together.

*Most teams see the difference in the first week.*

**Agent Deployment Setup**

We build out your agent infrastructure: the deployment layer, the trigger integrations (Slack, Linear, GitHub, email, whatever you use), and the first set of production agents. We train your team to add new ones.

*You own everything. No vendor lock-in. No monthly platform fee.*

**VPS + Orchestration Buildout**

We set up dedicated cloud instances for long-running agent work. Parallel execution harnesses. Pipeline orchestration. Monitoring. The infrastructure that lets you run agents at scale without managing a distributed system yourself.

*This is what turns AI from “that thing we tried” into a permanent part of how your team operates.*

### Open Resources

**AI Coding Starter Config** — Hooks, skills, and config templates for teams getting started with AI-assisted development. Includes our editorial accent system, Vale prose linting, and commit hooks.

**Agent Deployment Examples** — Three working examples: a Slack-triggered agent, a GitHub issue responder, and a scheduled report generator. All deployable to any VPS with Docker.

**Pipeline Templates** — Multi-stage pipeline definitions for common workflows: PR review, bug triage, and content generation. Built on our orchestration system.

### FAQ

**We already use Claude / Cursor / Copilot. Why do we need configs?**

Those tools are general-purpose. Configuration is what makes them specific to your codebase, your conventions, and the way your team works. Without it, every developer gets different (and inconsistent) AI output. Configs standardize quality across the team.

**Can our team maintain this after the engagement?**

That’s the entire point. Agent configs are code. AI coding configs live in your repo. VPS infrastructure is yours. We document everything and train your team before we leave.

**What stack does this work with?**

We’ve deployed for TypeScript/Node, Python, Ruby on Rails, and Go codebases. The agent infrastructure is stack-agnostic. AI coding configs work for any language with supported agents.

**How is this different from hiring a DevOps contractor?**

A DevOps contractor sets up CI/CD. We set up AI infrastructure: the agents, the orchestration, the developer tooling. Complementary, but different specialization.

**What does a typical engagement look like?**

Two-week sprints. Working infrastructure by the end of the first sprint. Most teams are running independently by sprint four.

**We have security concerns about AI agents accessing our codebase.**

Every agent runs with explicit, scoped permissions. Nothing gets network access, write access, or API access beyond what the config specifies. We can deploy on your infrastructure behind your VPN. We never retain access after the engagement ends.

### Your team writes the code. We'll build the systems around it.

Tell us about your stack, your team, and where you want AI to actually do something useful. We'll tell you what's realistic and what it would take.

*No sales process. First call is with the founder.*

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## AI Newsletter — Personalized for You

Every month, we research what's new in AI and write you a custom edition based on your role, industry, and interests.

### How It Works

01. **We track what’s new in AI every month** — Product launches, trending tools, new models, research worth knowing about. We pull from across the internet so you don’t have to.
02. **Your edition is written for your context** — You tell us your industry, role, and what you’re interested in. What surfaces in your edition depends on what’s relevant to your work.
03. **It gets sharper over time** — Rate what’s useful. Skip what isn’t. The system learns your preferences and adjusts. Edition five is more relevant than edition one.

### Sample Editions

Three readers. Three different briefings.

**CMO · E-Commerce** (CMO at a DTC e-commerce brand)

- AI-powered ad creative tools replacing manual A/B testing
- New attribution models for AI-assisted customer journeys
- How DTC brands are using AI for retention and segmentation

**Consultant · Strategy** (Strategy consultant, solo practice)

- Research tools that cut 4 hours off every client engagement
- Building AI-assisted proposal and deliverable workflows
- Models worth knowing: what changed this month and why it matters

**Ops Manager · Mid-Size Co.** (Operations manager at a mid-size company)

- Document processing tools that handle invoices and contracts
- Internal workflow automation without a dev team
- Case study: ops team reclaiming 15 hrs/week with AI triage

*Built by Dodo Digital, an AI automation consultancy. This newsletter runs on the same systems we build for clients. If it's good, you'll know what we build.*

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## Contact

- Website: https://www.dododigital.ai
- Book a consultation: https://www.dododigital.ai/get-started